Abstract
The determination of characteristic and discriminating terms as well as their semantic relationships plays a vital role in text processing applications. As an example, term clustering techniques heavily rely on this information. Classic approaches for this means such as statistical co-occurrence analysis however usually only consider relationships between two terms that co-occur as immediate neighbours or on sentence level. This article presents flexible approaches to find statistically significant correlations between two or more terms using co-occurrence windows of arbitrary sizes. Their applicability will be discussed in detail by presenting solutions to improve the interactive and image-based search in the World Wide Web. Moreover, approaches to determine directed term associations and applications for them will be explained, too.
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Kubek, M.M., Unger, H., Dusik, J. (2015). Correlating Words - Approaches and Applications. In: Azzopardi, G., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science(), vol 9256. Springer, Cham. https://doi.org/10.1007/978-3-319-23192-1_3
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DOI: https://doi.org/10.1007/978-3-319-23192-1_3
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